竹林地面光谱特征及遥感信息提取方法研究
|
摘要
针对我国传统的竹资源监测存在时间间隔长、人力投入大、调查难度高和统计精度低等许多不足,以福建省顺昌县为研究区域,开展不同季相竹林及其周边地物反射光谱特征,以及竹林面积遥感信息提取技术和动态监测技术的相关研究,主要结果如下:
(1)利用ASD光谱分析仪于2007年11月、2008年4月、7月、10月进行了4次野外光谱测定,研究对象包括:毛竹、杉木、马尾松和木荷,以及水体、旱田和水泥路面等其它地物。4个不同时期毛竹、杉木、马尾松和木荷的光谱特征研究表明:毛竹与其它植被类型具有可分性,较为理想的区分波段为500～600nm的可见光波段和670～800nm的近红外波段,而尤以556nm和718nm处区分效果最优。在遥感信息源时相选择方面,以春季的毛竹换叶期前后的时相为佳,其次为秋季。但由于南方的雨季影响,难以在该季节得到理想的影像数据。因此,秋季应为数据获取的重点时期。
(2)利用ASD光谱分析仪于2007年11月对闽北地区常见的毛竹林、杉木林和马尾松林纯林,以及竹阔混交、竹杉混交、杉阔混交等混交类型进行光谱特征测定,并将各类型光谱数据按TM的1~4波段进行光谱反射率模拟和相似性矩阵分析。结果表明:毛竹与杉木的可区分度较小,而与其它类型树种之间具有较好的区分性。
(3)以2001年10月的ETM数据为信息源,在采用ISODATA法、最大似然法和子像元分类法等3种常规分类方法的基础上,提出“基于光谱片层—面向纹理类”的竹林专题信息提取方法。实验结果表明,“基于光谱片层—面向纹理类”的竹林专题信息提取方法的分类精度可达84.8%,总Kappa系数达0.8235,能有效地解决由于地形因素引起的阴影问题,是提高山区竹林信息的分类精度的一种有效方法。
(4)选取顺昌县1988~2007年之间的4个时相的TM/ETM影像,采用分类后比较法进行竹林面积动态监测研究。结果表明,顺昌县域的竹资源分布范围广,主要分布于边远的四周山区,而中部和西北部则分布较少。从竹林面积增长情况来看,1988 ~1992年的年平均增长面积为0.0645万公顷,增长率为2.2%;1992 ~2001年的年平均增长面积为0.0262万公顷,增长率为0.8%;2001 ~2007年的年平均增长面积为0.1629万公顷,增长率为4.8%。2001年以来,是顺昌县竹林产业发展的快速增长时期。
通过野外实测不同季相条件下竹林及其周边地物的光谱特征,确定竹林与其他植被种类及生态环境因子的光谱特征差异与区分度的大小,可为竹林资源监测中卫星影像的选择和信息提取提供科学的光谱学依据,也为遥感技术在竹林资源长势、变化和健康等监测提供理论基础。通过对比分析不同的竹林面积提取精度的图像处理方法,为遥感技术在我国竹资源清查、面积变化、灾害预报及周边生态环境等动态监测中的应用提供理论基础和技术支持,对我国开展竹资源调查和监测研究具有重要的借鉴意义。
According to the deficiency of long time intervals, heavey human input, difficult to survey and low statistical accuracy of the traditional bamboo resources monitoring. Taking Shunchang county as study area, carried out the reflectance spectra features characteristic of bamboo and its surrounding ground objects with different phases, research on bamboo forest area extraction with remote sensing classification techniques and dynamic monitoring technology. The research results were as follow:
(1) The spectral characteristic of Phyllostachys pubescens, cunninghamia lanceolata, Pinus massoniana, Schima superba,water body, farmland and cement pavement were measured respectively in November 2007, April 2008, July 2008 and October 2008. According to the spectral characteristic of Phyllostachys pubescens, Cunninghamia lanceolata, Pinus massoniana, Schima superba in four different phases, it was shown that bamboo can be compartmentation with other vegetation types. The distinction between 500 ~ 600nm in the visible band and 670 ~ 800nm in the near-infrared band is significantly, optimal distinction result appeared on 556nm and 718nm. In the aspect of selecting phase of the remote sensing information, the best one was pre and post spring-leaf stage ,followed by the fall. However, due to the impact of the the rainy season in south area, it was difficult to get the ideal data, so the fall was the key period to get remote images data.
(2) The spectral characteristic of Phyllostachys pubescens, cunninghamia lanceolata, Pinus massoniana pure forest and bamboo mixed wide, bamboo mixed cunninghamia lanceolata, cunninghamia lanceolata mixed Schima superba were measured respectively in November 2007. Then makes the human simulation of the It also makes all types of spectral data according to TM1~4 bands resampled and similarity matrix analysis. The results showed that: bamboo and cunninghamia lanceolata can distinguish little degrees, and with other types was more.
(3) The research relating to remote sensing classification extraction of bamboo forest areas was carried out with the ETM data which was acquried in October 2001.
Based on three kinds of conventional methods which are ISODATA method, maximum likelihood method and sub-pixel classification categories, A new thematic information extraction method which called‘Base on the spectral sheet and texture typeswas proposed’in this paper. The experimental results show that, the classification accuracy of this method can be 84.8%, and the total Kappa coefficient can be 0.8235, which can effectively solve the shadow problem caused by terrain factor. It was an effective way to improve the classification accuracy of the information about bamboo in mountain area.
(4) Four phases of TM/ETM images between 1988 and 2007 in Shunchang country were selected to making research about dynamic monitoring of bamboo forest areas through post-classification comparison method.
The results showed that there was wide range of bamboo resources in Shunchang county, which mainly located in remote mountainous areas and the central and north-west areas little. On the point of rising situation of the Bamboo area, the average annual growth between 1988 and 1992 was 0.0645 million hectares with the growth rate 2.2%.The average annual growth between 1992 and 2001 was 0.0262 million hectares with the growth rate 0.8%. The average annual growth between 2001 and 2007 was 0.1629 million hectares with the growth rate 4.8%.The third period was the rapid growing period of industrial development about bamboo in Shunchang County.
Through measured the spectral characteristics of bamboo forest and surrounding surface features in different seasons, differences among bamboo, other vegetation and eco-environmental factors were determined to distinguish, and the spectroscopy theory of satellite images choice and information extraction were provide, and a scientific basis of remote sensing technology in the bamboo forest resources, growth, change and health monitoring was provided. By comparing the different extraction methods of bamboo forest area precision, provided technical support for bamboo resource inventory in China, and covering change, disaster forecasting and the surrounding ecological environment of the application of dynamic monitoring with remote sensing, and it was important to carry out the bamboo resource investigation and monitoring in China .
(1)利用ASD光谱分析仪于2007年11月、2008年4月、7月、10月进行了4次野外光谱测定,研究对象包括:毛竹、杉木、马尾松和木荷,以及水体、旱田和水泥路面等其它地物。4个不同时期毛竹、杉木、马尾松和木荷的光谱特征研究表明:毛竹与其它植被类型具有可分性,较为理想的区分波段为500～600nm的可见光波段和670～800nm的近红外波段,而尤以556nm和718nm处区分效果最优。在遥感信息源时相选择方面,以春季的毛竹换叶期前后的时相为佳,其次为秋季。但由于南方的雨季影响,难以在该季节得到理想的影像数据。因此,秋季应为数据获取的重点时期。
(2)利用ASD光谱分析仪于2007年11月对闽北地区常见的毛竹林、杉木林和马尾松林纯林,以及竹阔混交、竹杉混交、杉阔混交等混交类型进行光谱特征测定,并将各类型光谱数据按TM的1~4波段进行光谱反射率模拟和相似性矩阵分析。结果表明:毛竹与杉木的可区分度较小,而与其它类型树种之间具有较好的区分性。
(3)以2001年10月的ETM数据为信息源,在采用ISODATA法、最大似然法和子像元分类法等3种常规分类方法的基础上,提出“基于光谱片层—面向纹理类”的竹林专题信息提取方法。实验结果表明,“基于光谱片层—面向纹理类”的竹林专题信息提取方法的分类精度可达84.8%,总Kappa系数达0.8235,能有效地解决由于地形因素引起的阴影问题,是提高山区竹林信息的分类精度的一种有效方法。
(4)选取顺昌县1988~2007年之间的4个时相的TM/ETM影像,采用分类后比较法进行竹林面积动态监测研究。结果表明,顺昌县域的竹资源分布范围广,主要分布于边远的四周山区,而中部和西北部则分布较少。从竹林面积增长情况来看,1988 ~1992年的年平均增长面积为0.0645万公顷,增长率为2.2%;1992 ~2001年的年平均增长面积为0.0262万公顷,增长率为0.8%;2001 ~2007年的年平均增长面积为0.1629万公顷,增长率为4.8%。2001年以来,是顺昌县竹林产业发展的快速增长时期。
通过野外实测不同季相条件下竹林及其周边地物的光谱特征,确定竹林与其他植被种类及生态环境因子的光谱特征差异与区分度的大小,可为竹林资源监测中卫星影像的选择和信息提取提供科学的光谱学依据,也为遥感技术在竹林资源长势、变化和健康等监测提供理论基础。通过对比分析不同的竹林面积提取精度的图像处理方法,为遥感技术在我国竹资源清查、面积变化、灾害预报及周边生态环境等动态监测中的应用提供理论基础和技术支持,对我国开展竹资源调查和监测研究具有重要的借鉴意义。
According to the deficiency of long time intervals, heavey human input, difficult to survey and low statistical accuracy of the traditional bamboo resources monitoring. Taking Shunchang county as study area, carried out the reflectance spectra features characteristic of bamboo and its surrounding ground objects with different phases, research on bamboo forest area extraction with remote sensing classification techniques and dynamic monitoring technology. The research results were as follow:
(1) The spectral characteristic of Phyllostachys pubescens, cunninghamia lanceolata, Pinus massoniana, Schima superba,water body, farmland and cement pavement were measured respectively in November 2007, April 2008, July 2008 and October 2008. According to the spectral characteristic of Phyllostachys pubescens, Cunninghamia lanceolata, Pinus massoniana, Schima superba in four different phases, it was shown that bamboo can be compartmentation with other vegetation types. The distinction between 500 ~ 600nm in the visible band and 670 ~ 800nm in the near-infrared band is significantly, optimal distinction result appeared on 556nm and 718nm. In the aspect of selecting phase of the remote sensing information, the best one was pre and post spring-leaf stage ,followed by the fall. However, due to the impact of the the rainy season in south area, it was difficult to get the ideal data, so the fall was the key period to get remote images data.
(2) The spectral characteristic of Phyllostachys pubescens, cunninghamia lanceolata, Pinus massoniana pure forest and bamboo mixed wide, bamboo mixed cunninghamia lanceolata, cunninghamia lanceolata mixed Schima superba were measured respectively in November 2007. Then makes the human simulation of the It also makes all types of spectral data according to TM1~4 bands resampled and similarity matrix analysis. The results showed that: bamboo and cunninghamia lanceolata can distinguish little degrees, and with other types was more.
(3) The research relating to remote sensing classification extraction of bamboo forest areas was carried out with the ETM data which was acquried in October 2001.
Based on three kinds of conventional methods which are ISODATA method, maximum likelihood method and sub-pixel classification categories, A new thematic information extraction method which called‘Base on the spectral sheet and texture typeswas proposed’in this paper. The experimental results show that, the classification accuracy of this method can be 84.8%, and the total Kappa coefficient can be 0.8235, which can effectively solve the shadow problem caused by terrain factor. It was an effective way to improve the classification accuracy of the information about bamboo in mountain area.
(4) Four phases of TM/ETM images between 1988 and 2007 in Shunchang country were selected to making research about dynamic monitoring of bamboo forest areas through post-classification comparison method.
The results showed that there was wide range of bamboo resources in Shunchang county, which mainly located in remote mountainous areas and the central and north-west areas little. On the point of rising situation of the Bamboo area, the average annual growth between 1988 and 1992 was 0.0645 million hectares with the growth rate 2.2%.The average annual growth between 1992 and 2001 was 0.0262 million hectares with the growth rate 0.8%. The average annual growth between 2001 and 2007 was 0.1629 million hectares with the growth rate 4.8%.The third period was the rapid growing period of industrial development about bamboo in Shunchang County.
Through measured the spectral characteristics of bamboo forest and surrounding surface features in different seasons, differences among bamboo, other vegetation and eco-environmental factors were determined to distinguish, and the spectroscopy theory of satellite images choice and information extraction were provide, and a scientific basis of remote sensing technology in the bamboo forest resources, growth, change and health monitoring was provided. By comparing the different extraction methods of bamboo forest area precision, provided technical support for bamboo resource inventory in China, and covering change, disaster forecasting and the surrounding ecological environment of the application of dynamic monitoring with remote sensing, and it was important to carry out the bamboo resource investigation and monitoring in China .
引文
[1]陈述彭遥感地学分析时空维遥感学报,1997,1(3):161-169
[2]陈述彭城市化与城市地理系统北京:科学出版社,1999
[3]陈晋,何春阳,卓莉.基于变化向量分析(CVA)的土地利用/覆盖变化动态监测(II)—变化类型的确定方法遥感学报,2001,5(5):346-352
[4]陈志鹏,邓鹏,种劲松,王宏琦.纹理特征在SAR图像变化检测中的应用遥感技术与应用,2002,17(3):56-60
[5]陈雪,马建文,戴芹.基于贝叶斯网络分类的遥感影像变化检测遥感学报,2005,9(6):667-672
[6]陈阳,陈鹰,林怡. GIS辅助下的高分辨率遥感影像变化检测铁道勘察,2008(4):29-32
[7]曹雪虹信息论与编码北京:清华大学出版社,2004.
[8]蔡爱民航天遥感技术在我国森林资源中的应用滁州学院学报,2006,8(3):84-88
[9]戴昌达,雷莉萍. TM图像的光谱信息特征与最佳波段组合环境遥感,1989,4(4):282-292
[10]党安荣,王晓栋,陈晓峰.遥感图像处理方法北京:清华大学出版社,2004
[11]丁丽霞.天目山国家级自然保护区毛竹林扩张遥感监测浙江林学院学报,2006,23(3):297-300
[12]杜华强,周国模,葛宏立等.基于TM数据提取竹林遥感信息的方法东北林业大学学报,2008,36(3):35-38
[13]方针,张剑清,张祖勋.基于城区航空影像的变化检测武汉测绘科技大学学报,1997,22(3):240-244
[14]方圣辉,佃袁勇,李微.基于边缘特征的变化检测方法研究武汉大学学报(信息科学版),2005,30(2):135-138
[15]范文义,罗传文.“3S”理论与技术哈尔滨:东北林业大学出版社,2004
[16]冯春,郭建宁,闵祥军.土地利用/土地覆盖遥感变化检测方法新进展遥感信息,2006(3):8l-84
[17]冯仲科,余新晓“3S”技术及其应用北京:中国林业出版社,2000
[18]樊风雷,陈忠暖. GIS支持下珠江三角洲土地利用变化(1980-2003年)遥感监测研究述评云南地理环境研究,2008,20(1):1-6
[19]宫鹏,浦瑞良,郁彬.不同季相针叶树种高光谱数据识别分析遥感学报,1998,2(3):211-217
[20]宫鹏遥感生态测量学进展自然资源学报,1999,14(4):313-317
[21]葛宏立,方陆明.遥感图像森林资源信息提取与分析研究北京:科学出版社,2007
[22]龚健雅,朱欣焰,朱庆等面向对象集成化空间数据库管理系统的设计与实现2000,8(4):289-293
[23]桂预风,张继贤,林宗坚.土地利用遥感动态监测中混合像元的分解方法研究遥感信息,2002(2):18-20
[24]黄华文,常本义.利用遥感数据更新GIS的研究解放军测绘学院学报,1997,14(3):25-30
[25]何春阳,陈晋,陈云浩,史培军.土地利用/覆盖变化混合动态监测方法研究自然资源学报,2001,16(3):255-262
[26]何挺,刘荣,王静.野外波谱测量的影响因素研究地理与地理信息科学,2003,19(5):6-10
[27]贾炅.森林下层植物资源的航空遥感分析一以川西针叶树下箭竹为例北京师范大学学报(自然科学版),1993,29(3):416-421
[28]贾凌.基于TM的海南省土地利用/覆盖动态变化的遥感监测和分析遥感信息,2003,1:22-26
[29]江泽慧世界竹藤辽宁:辽宁科学技术出版社,2002,416-421
[30]陆灯盛. TM图像的信息量分析及特征信息提取的研究环境遥感,1991,6(4):267-274.
[31]李强,王正志.遥感图像分类与后处理综合技术研究—基于约束满足神经网络方法遥感学报,1999(3):193-198
[32]李云梅,倪绍祥,黄敬峰.高光谱数据探讨水稻叶片叶绿素含量对叶片及冠层光谱反射特性的影响遥感技术与应用,2003,18(1):1-5
[33]李石华,王金亮.多光谱遥感数据最佳波段选择方法试验研究云南地理环境研究,2005,17,(6):29-33
[34]李月臣,杨华,刘春霞,等.土地履盖变化遥感检测方法水土保持研究,2006,13(01):209-216
[35]李全,李霖,赵曦.基于Landsat TM影像的城市变化检测研究武汉大学学报(信息科学版) 2005,30(4):351-354
[36]李月臣,杨华,刘春霞.土地覆盖变化遥感检测方法[J].水土保持研究,2006,2(1):2l0-2l3
[37]罗音,舒宁.基于信息量确定遥感图像主要波段的方法城市勘测,2002,4:28-32.
[38]黎颖卿,黄宁辉.浅谈遥感在我国森林资源监测中的应用现状防护林科技,2006,6(4):61-64.
[39]廖明生,朱攀,龚健雅.基于典型相关分析的多元变化检测遥感学报,2000,4(3):197-201
[40]刘永怀混合像元分解的理论与方法[J]遥感技术与应用,2002,7(1):7-15
[41]刘宏斌,张云贵,李志宏等.光谱技术在冬小麦氮素营养诊断中的应用研究中国农业科学,2004,37(11):1743-1748
[42]刘健,余坤勇,亓兴兰,等.基于专家分类知识库的林地分类福建农林大学学报(自然科学版),2006,35(1):42-46.
[43]刘良云,宋晓宇,李存军等冬小麦病毒与产量损失的多时相遥感监测[J]农业工程学报,2009,25(1):137-143
[44]马建文.遥感变化检测技术发展综述地球科学进展,2004(4):192～196
[45]宁晓波,林辉. 3S技术在贵州省森林资源清查及其评价中的应用林业资源管理,2003(3):29-32.
[46]浦瑞良,宫鹏.高光谱及其运用北京:高等教育出版社,2000
[47]彭望碌,白振平,刘湘南,等.遥感概论北京:高等教育出版社,2002
[48]屈永华,刘素红,王锦地等.中国典型地物波谱数据库的研究与设计遥感信息,2004(2):5-8
[49]任国业.大熊猫主食竹资源的遥感调查遥感信息,1980(2):34-35
[50]任国业.大熊猫主食竹的彩红外遥感判读技术探讨遥感信息,1990(4):15-17.
[51]史培军,陈晋,潘耀忠.深圳市土地利用变化机制分析地理学报,2000,55(2):151-160
[52]苏力华,楼玫娟,肖金香,等.气象卫星遥感监测在森林防火中的应用西北农林科技大学学报(自然科学版),2004,32(11):85-88
[53]谭炳香.高光谱遥感森林应用研究探讨世界林业研究,2003,16(2):33-37.
[54]王宪成.地理信息系统(GIS)在林业上的应用及发展趋势吉林林业科技,2000,6(3):12-15.
[55]王蕾,黄华国,张晓丽,等. 3S技术在森林虫害动态监测中的应用世界林业研究,2005, 18(2):51-56.
[56]王庆光,潘燕芳.多光谱遥感数据最佳波段选择的研究韶关学院学报(自然科学版),2006,27(3):42-44.
[57]翁强,卢昌义.红树植物地面反射光谱特征研究福建林业科技,2006,33(3):14-19
[58]万余庆,谭克龙,周日平.高光谱遥感应用研究北京:科学出版社,2006
[59]肖兴威中国森林资源清查北京:高等教育出版社,2005
[60]肖兴威,姚昌恬,陈雪峰,等.美国森林资源清查的基本做法和启示林业资源管理,2005(2):27-42.
[61]易维宁,陆亦怀,罗明.地物光谱数据库及其在遥感中的应用光电子技术与信息,1998,11(5):25-28
[62]游先祥遥感原理及在资源环境中的应用北京:中国林业出版社,2003
[63]颜梅春,张友静,鲍艳松.基于灰度共生矩阵法的IKONOS影像中竹林信息提取遥感信息,2004,(2):31-34.
[64]袁修孝,宋妍.基于边缘特征匹配的遥感影像变化检测预处理方法武汉大学学报(信息科学版),2007,32(5):381-384
[65]袁琪,赵荣椿.一种改进的遥感图像变化检测算法电子与信息学报,2008,30(11):2737-2741
[66]杨希,刘国祥,秦军,等.基于多时相遥感图像灰度差值法的地表变化检测四川测绘,2008,31(3):99-103
[67]朱述龙,张占睦.遥感图象获取与分析北京:科学出版社,2000
[68]赵英时.遥感应用分析原理及其方法北京:科学出版社,2003,97-103
[69]曾伟生.遥感技术在森林资源清查中的应用问题探讨中南林业调查规划,2004,23(1):47-49
[70]周斌.针对土地覆盖变化的多时相遥感探测方法矿物学报,2000,20(2):165-171
[71]周斌,杨柏林.运用多时相直接分类法对土地利用进行遥感动态监测的研究自然资源学报,2001,16(03):263-268
[72] Al-Abbas A H, Barr R, Hall J D, et al. Spectra of normal and nutrient-deficient maize leaves, Agron.J,1974,66:16-20
[73] Anatoly A Gitelson, Yoram J Kaufrman, Robert Stark, Don Rundquist. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment,2002,80(1):76-87
[74] Bruzzone L, Prieto D F. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing.2003.38(3):171-1182
[75] Bharadwaj S P, Siva Subramanian, Sudhakar Manda, et al. Bamboo livelihood development planning, monitoring and analysis through GIS and remote sensing. Journal of Bamboo and Rattan. 2003,2(4):453-461.
[76] Coppin P E, Lambin I, Jonckheere, et al. Digital change detection methods in natural ecosystem monitoring: A review in proccedings of the first international workshop on multitemp 2001, World Scientific Publishing, 2001(2):3-36
[77] Dura D B, Hiura H. Expansion characteristics of bamboo stand and sediment disaster in South Western Japan. (CAB). Pakistan Journal of Biological Sciences. 2006,9(4):622-631
[78] Howarth P J, Wichware G M. Procedures for change detection using landsat digital data. International Journal of Remote Sensing, 1981,2:277-291
[79] Hans Tommervik, Kjell Arild Hogda, Inger Sothern. Monitoring vegetation changes in Pasvik(Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 2003,85(3):370-388
[80] Jose A Martinpz-Casasnovas. A cartographic and database approach for land cover/use mapping and generalization from remotely sensed data. International journal of Remote Sensing, 2000, 21(9):1825-1842
[81] Khali Aziz Hamzah Mapping of bamboos area:remote sensing approach. Buletin Buluh(Malaysia). 1995, 3(1): 11-12
[82] Lyon J G, Yuan D, Lunetta R S. A. Change detection experiment using vegetation indices,PE &RS,1998.64(2):143-150
[83] Linderman M, Liu J, Qi J, et al. Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data. International Journal of Remote Sensing.2004,25(9):1685-1700
[84] Lin hui, Peng changhui. Geographical information system in forestry: Practices , Problems and Prospects.Forestry Studies in China,2000(1)38-43
[85] Miller L, Nualchaweek, Tom C. Analysis of the dynamics of shifting cultivation in the tropical forests of norhern Thailand using landscape modeling and classification of landsat imager. Greenbel,Marland, U.S.A.:NASA Technical Memorandum 79545,Goddard Space Flight Cengtre,1978
[86] Ma J W, Zhao G, et al. Automatic change detection of artificial objects in multitemporal high spatial resolution remotely. IEEE Transactions on Geoscience and Remote Sensing. 2003.5(7):3356-3358
[87] Menon A R R. Remote sensing application in bamboo resource evaluation : a case study in Kerala . Bangkok (Thailand).1994,51-53
[88] Nair P V, Menon, A R R Estimation of bamboo resources in Kerala by remote sensing techniques.Current Science 1998; 75(3):209-210
[89] Nishikawa R,Murakami T,Yoshida S, Mitsuda Y, Nagashima K,Mizoue N. Characteristic of temporal range shifts of bamboo stands according to adjacent landcover type.Journal of the Japanese Forest Society 2005, 87 (5) :402-409
[90] Peter A. Rogerson. Change detection thresholds for remotely sensed images. Journal of Geographical Systems,2002,4:85-97
[91] Rowe N C.Grewe L L. Change detection for linear features in aerial photographs using edge-finding. IEEE Transactions on Geoscience and Remote Sensing.2001.39(7):1608-1612
[92] Singh, A. Digital change detection techniques using remotely sensed data . INT.J.Remote Sensing,1989,10(6):989-1003
[93] SHEFFIELD C.Selecting band combinations from multispectral data. Photogrammetric Engineering&Remote senging, 1985, 51(6): 681-687
[94] Steven M D, Clark JA. High spectral resolution indices for crop stress. In: Application of Remote Sensing in Agriculture. Edited by Steven M D, Clark JA,1990
[95] Thomas J R, Oerther G F. Estimating nitrogen content of sweet pepper leaves by reflectance measurements, Agron.J.1972,64:11~13
[96] Shibayama,M.,Munakata,K.A spectroradiometer for field use. III.A comparison of some vegetation indices for predicting luxuriant paddy rice biomass.Japanese Journal of Crop Science,1986,55:47~52
[97] Somyot Saengnin. Application of remote sensing data for estimating bamboo forest production in Northern and Western part of Thailand.Bangkok(Thailand).1993
[98] Sigh A.Digital change detection technique using remotely-sensed data. Int. J.Remote Sensing. 1989,10(6):263-268
[99] Thomas J R, Oerther G F. Estimating nitrogen content of sweet pepper leaves by reflectance measurements, Agron.J.1972,64:11~13
[100] Thomas,J.R, H.W.Gausman. Leaf reflectance vs leaf chlorophyll and carotenoid concentrations for eight crops.Agron.J.1977,69:799~802
[101] Torii A. Estimation of range expansion rate of bamboo stands using aerial photographs. Japanese Journal of Ecology 1998, 48(1):37-47
[102] Yuan D, Elvidge C D, Lunnetta R S. Survey of multispectral methods for land cover change analysis[ A].L unnetta S,E lvidge C D.Remote sensing change detection: environmental monitoring methods and applications. Ann Arbor( Michigan):Ann Arbor Press,1998,21-40
[2]陈述彭城市化与城市地理系统北京:科学出版社,1999
[3]陈晋,何春阳,卓莉.基于变化向量分析(CVA)的土地利用/覆盖变化动态监测(II)—变化类型的确定方法遥感学报,2001,5(5):346-352
[4]陈志鹏,邓鹏,种劲松,王宏琦.纹理特征在SAR图像变化检测中的应用遥感技术与应用,2002,17(3):56-60
[5]陈雪,马建文,戴芹.基于贝叶斯网络分类的遥感影像变化检测遥感学报,2005,9(6):667-672
[6]陈阳,陈鹰,林怡. GIS辅助下的高分辨率遥感影像变化检测铁道勘察,2008(4):29-32
[7]曹雪虹信息论与编码北京:清华大学出版社,2004.
[8]蔡爱民航天遥感技术在我国森林资源中的应用滁州学院学报,2006,8(3):84-88
[9]戴昌达,雷莉萍. TM图像的光谱信息特征与最佳波段组合环境遥感,1989,4(4):282-292
[10]党安荣,王晓栋,陈晓峰.遥感图像处理方法北京:清华大学出版社,2004
[11]丁丽霞.天目山国家级自然保护区毛竹林扩张遥感监测浙江林学院学报,2006,23(3):297-300
[12]杜华强,周国模,葛宏立等.基于TM数据提取竹林遥感信息的方法东北林业大学学报,2008,36(3):35-38
[13]方针,张剑清,张祖勋.基于城区航空影像的变化检测武汉测绘科技大学学报,1997,22(3):240-244
[14]方圣辉,佃袁勇,李微.基于边缘特征的变化检测方法研究武汉大学学报(信息科学版),2005,30(2):135-138
[15]范文义,罗传文.“3S”理论与技术哈尔滨:东北林业大学出版社,2004
[16]冯春,郭建宁,闵祥军.土地利用/土地覆盖遥感变化检测方法新进展遥感信息,2006(3):8l-84
[17]冯仲科,余新晓“3S”技术及其应用北京:中国林业出版社,2000
[18]樊风雷,陈忠暖. GIS支持下珠江三角洲土地利用变化(1980-2003年)遥感监测研究述评云南地理环境研究,2008,20(1):1-6
[19]宫鹏,浦瑞良,郁彬.不同季相针叶树种高光谱数据识别分析遥感学报,1998,2(3):211-217
[20]宫鹏遥感生态测量学进展自然资源学报,1999,14(4):313-317
[21]葛宏立,方陆明.遥感图像森林资源信息提取与分析研究北京:科学出版社,2007
[22]龚健雅,朱欣焰,朱庆等面向对象集成化空间数据库管理系统的设计与实现2000,8(4):289-293
[23]桂预风,张继贤,林宗坚.土地利用遥感动态监测中混合像元的分解方法研究遥感信息,2002(2):18-20
[24]黄华文,常本义.利用遥感数据更新GIS的研究解放军测绘学院学报,1997,14(3):25-30
[25]何春阳,陈晋,陈云浩,史培军.土地利用/覆盖变化混合动态监测方法研究自然资源学报,2001,16(3):255-262
[26]何挺,刘荣,王静.野外波谱测量的影响因素研究地理与地理信息科学,2003,19(5):6-10
[27]贾炅.森林下层植物资源的航空遥感分析一以川西针叶树下箭竹为例北京师范大学学报(自然科学版),1993,29(3):416-421
[28]贾凌.基于TM的海南省土地利用/覆盖动态变化的遥感监测和分析遥感信息,2003,1:22-26
[29]江泽慧世界竹藤辽宁:辽宁科学技术出版社,2002,416-421
[30]陆灯盛. TM图像的信息量分析及特征信息提取的研究环境遥感,1991,6(4):267-274.
[31]李强,王正志.遥感图像分类与后处理综合技术研究—基于约束满足神经网络方法遥感学报,1999(3):193-198
[32]李云梅,倪绍祥,黄敬峰.高光谱数据探讨水稻叶片叶绿素含量对叶片及冠层光谱反射特性的影响遥感技术与应用,2003,18(1):1-5
[33]李石华,王金亮.多光谱遥感数据最佳波段选择方法试验研究云南地理环境研究,2005,17,(6):29-33
[34]李月臣,杨华,刘春霞,等.土地履盖变化遥感检测方法水土保持研究,2006,13(01):209-216
[35]李全,李霖,赵曦.基于Landsat TM影像的城市变化检测研究武汉大学学报(信息科学版) 2005,30(4):351-354
[36]李月臣,杨华,刘春霞.土地覆盖变化遥感检测方法[J].水土保持研究,2006,2(1):2l0-2l3
[37]罗音,舒宁.基于信息量确定遥感图像主要波段的方法城市勘测,2002,4:28-32.
[38]黎颖卿,黄宁辉.浅谈遥感在我国森林资源监测中的应用现状防护林科技,2006,6(4):61-64.
[39]廖明生,朱攀,龚健雅.基于典型相关分析的多元变化检测遥感学报,2000,4(3):197-201
[40]刘永怀混合像元分解的理论与方法[J]遥感技术与应用,2002,7(1):7-15
[41]刘宏斌,张云贵,李志宏等.光谱技术在冬小麦氮素营养诊断中的应用研究中国农业科学,2004,37(11):1743-1748
[42]刘健,余坤勇,亓兴兰,等.基于专家分类知识库的林地分类福建农林大学学报(自然科学版),2006,35(1):42-46.
[43]刘良云,宋晓宇,李存军等冬小麦病毒与产量损失的多时相遥感监测[J]农业工程学报,2009,25(1):137-143
[44]马建文.遥感变化检测技术发展综述地球科学进展,2004(4):192～196
[45]宁晓波,林辉. 3S技术在贵州省森林资源清查及其评价中的应用林业资源管理,2003(3):29-32.
[46]浦瑞良,宫鹏.高光谱及其运用北京:高等教育出版社,2000
[47]彭望碌,白振平,刘湘南,等.遥感概论北京:高等教育出版社,2002
[48]屈永华,刘素红,王锦地等.中国典型地物波谱数据库的研究与设计遥感信息,2004(2):5-8
[49]任国业.大熊猫主食竹资源的遥感调查遥感信息,1980(2):34-35
[50]任国业.大熊猫主食竹的彩红外遥感判读技术探讨遥感信息,1990(4):15-17.
[51]史培军,陈晋,潘耀忠.深圳市土地利用变化机制分析地理学报,2000,55(2):151-160
[52]苏力华,楼玫娟,肖金香,等.气象卫星遥感监测在森林防火中的应用西北农林科技大学学报(自然科学版),2004,32(11):85-88
[53]谭炳香.高光谱遥感森林应用研究探讨世界林业研究,2003,16(2):33-37.
[54]王宪成.地理信息系统(GIS)在林业上的应用及发展趋势吉林林业科技,2000,6(3):12-15.
[55]王蕾,黄华国,张晓丽,等. 3S技术在森林虫害动态监测中的应用世界林业研究,2005, 18(2):51-56.
[56]王庆光,潘燕芳.多光谱遥感数据最佳波段选择的研究韶关学院学报(自然科学版),2006,27(3):42-44.
[57]翁强,卢昌义.红树植物地面反射光谱特征研究福建林业科技,2006,33(3):14-19
[58]万余庆,谭克龙,周日平.高光谱遥感应用研究北京:科学出版社,2006
[59]肖兴威中国森林资源清查北京:高等教育出版社,2005
[60]肖兴威,姚昌恬,陈雪峰,等.美国森林资源清查的基本做法和启示林业资源管理,2005(2):27-42.
[61]易维宁,陆亦怀,罗明.地物光谱数据库及其在遥感中的应用光电子技术与信息,1998,11(5):25-28
[62]游先祥遥感原理及在资源环境中的应用北京:中国林业出版社,2003
[63]颜梅春,张友静,鲍艳松.基于灰度共生矩阵法的IKONOS影像中竹林信息提取遥感信息,2004,(2):31-34.
[64]袁修孝,宋妍.基于边缘特征匹配的遥感影像变化检测预处理方法武汉大学学报(信息科学版),2007,32(5):381-384
[65]袁琪,赵荣椿.一种改进的遥感图像变化检测算法电子与信息学报,2008,30(11):2737-2741
[66]杨希,刘国祥,秦军,等.基于多时相遥感图像灰度差值法的地表变化检测四川测绘,2008,31(3):99-103
[67]朱述龙,张占睦.遥感图象获取与分析北京:科学出版社,2000
[68]赵英时.遥感应用分析原理及其方法北京:科学出版社,2003,97-103
[69]曾伟生.遥感技术在森林资源清查中的应用问题探讨中南林业调查规划,2004,23(1):47-49
[70]周斌.针对土地覆盖变化的多时相遥感探测方法矿物学报,2000,20(2):165-171
[71]周斌,杨柏林.运用多时相直接分类法对土地利用进行遥感动态监测的研究自然资源学报,2001,16(03):263-268
[72] Al-Abbas A H, Barr R, Hall J D, et al. Spectra of normal and nutrient-deficient maize leaves, Agron.J,1974,66:16-20
[73] Anatoly A Gitelson, Yoram J Kaufrman, Robert Stark, Don Rundquist. Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment,2002,80(1):76-87
[74] Bruzzone L, Prieto D F. Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing.2003.38(3):171-1182
[75] Bharadwaj S P, Siva Subramanian, Sudhakar Manda, et al. Bamboo livelihood development planning, monitoring and analysis through GIS and remote sensing. Journal of Bamboo and Rattan. 2003,2(4):453-461.
[76] Coppin P E, Lambin I, Jonckheere, et al. Digital change detection methods in natural ecosystem monitoring: A review in proccedings of the first international workshop on multitemp 2001, World Scientific Publishing, 2001(2):3-36
[77] Dura D B, Hiura H. Expansion characteristics of bamboo stand and sediment disaster in South Western Japan. (CAB). Pakistan Journal of Biological Sciences. 2006,9(4):622-631
[78] Howarth P J, Wichware G M. Procedures for change detection using landsat digital data. International Journal of Remote Sensing, 1981,2:277-291
[79] Hans Tommervik, Kjell Arild Hogda, Inger Sothern. Monitoring vegetation changes in Pasvik(Norway) and Pechenga in Kola Peninsula (Russia) using multitemporal Landsat MSS/TM data. Remote Sensing of Environment, 2003,85(3):370-388
[80] Jose A Martinpz-Casasnovas. A cartographic and database approach for land cover/use mapping and generalization from remotely sensed data. International journal of Remote Sensing, 2000, 21(9):1825-1842
[81] Khali Aziz Hamzah Mapping of bamboos area:remote sensing approach. Buletin Buluh(Malaysia). 1995, 3(1): 11-12
[82] Lyon J G, Yuan D, Lunetta R S. A. Change detection experiment using vegetation indices,PE &RS,1998.64(2):143-150
[83] Linderman M, Liu J, Qi J, et al. Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data. International Journal of Remote Sensing.2004,25(9):1685-1700
[84] Lin hui, Peng changhui. Geographical information system in forestry: Practices , Problems and Prospects.Forestry Studies in China,2000(1)38-43
[85] Miller L, Nualchaweek, Tom C. Analysis of the dynamics of shifting cultivation in the tropical forests of norhern Thailand using landscape modeling and classification of landsat imager. Greenbel,Marland, U.S.A.:NASA Technical Memorandum 79545,Goddard Space Flight Cengtre,1978
[86] Ma J W, Zhao G, et al. Automatic change detection of artificial objects in multitemporal high spatial resolution remotely. IEEE Transactions on Geoscience and Remote Sensing. 2003.5(7):3356-3358
[87] Menon A R R. Remote sensing application in bamboo resource evaluation : a case study in Kerala . Bangkok (Thailand).1994,51-53
[88] Nair P V, Menon, A R R Estimation of bamboo resources in Kerala by remote sensing techniques.Current Science 1998; 75(3):209-210
[89] Nishikawa R,Murakami T,Yoshida S, Mitsuda Y, Nagashima K,Mizoue N. Characteristic of temporal range shifts of bamboo stands according to adjacent landcover type.Journal of the Japanese Forest Society 2005, 87 (5) :402-409
[90] Peter A. Rogerson. Change detection thresholds for remotely sensed images. Journal of Geographical Systems,2002,4:85-97
[91] Rowe N C.Grewe L L. Change detection for linear features in aerial photographs using edge-finding. IEEE Transactions on Geoscience and Remote Sensing.2001.39(7):1608-1612
[92] Singh, A. Digital change detection techniques using remotely sensed data . INT.J.Remote Sensing,1989,10(6):989-1003
[93] SHEFFIELD C.Selecting band combinations from multispectral data. Photogrammetric Engineering&Remote senging, 1985, 51(6): 681-687
[94] Steven M D, Clark JA. High spectral resolution indices for crop stress. In: Application of Remote Sensing in Agriculture. Edited by Steven M D, Clark JA,1990
[95] Thomas J R, Oerther G F. Estimating nitrogen content of sweet pepper leaves by reflectance measurements, Agron.J.1972,64:11~13
[96] Shibayama,M.,Munakata,K.A spectroradiometer for field use. III.A comparison of some vegetation indices for predicting luxuriant paddy rice biomass.Japanese Journal of Crop Science,1986,55:47~52
[97] Somyot Saengnin. Application of remote sensing data for estimating bamboo forest production in Northern and Western part of Thailand.Bangkok(Thailand).1993
[98] Sigh A.Digital change detection technique using remotely-sensed data. Int. J.Remote Sensing. 1989,10(6):263-268
[99] Thomas J R, Oerther G F. Estimating nitrogen content of sweet pepper leaves by reflectance measurements, Agron.J.1972,64:11~13
[100] Thomas,J.R, H.W.Gausman. Leaf reflectance vs leaf chlorophyll and carotenoid concentrations for eight crops.Agron.J.1977,69:799~802
[101] Torii A. Estimation of range expansion rate of bamboo stands using aerial photographs. Japanese Journal of Ecology 1998, 48(1):37-47
[102] Yuan D, Elvidge C D, Lunnetta R S. Survey of multispectral methods for land cover change analysis[ A].L unnetta S,E lvidge C D.Remote sensing change detection: environmental monitoring methods and applications. Ann Arbor( Michigan):Ann Arbor Press,1998,21-40
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |